Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations167
Missing cells21
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 KiB
Average record size in memory104.8 B

Variable types

Numeric10
Categorical3

Alerts

AE_spindle is highly overall correlated with AE_tableHigh correlation
AE_table is highly overall correlated with AE_spindleHigh correlation
DOC is highly overall correlated with caseHigh correlation
VB is highly overall correlated with run and 1 other fieldsHigh correlation
case is highly overall correlated with DOC and 3 other fieldsHigh correlation
feed is highly overall correlated with caseHigh correlation
material is highly overall correlated with caseHigh correlation
run is highly overall correlated with VB and 1 other fieldsHigh correlation
time is highly overall correlated with VB and 1 other fieldsHigh correlation
vib_table is highly overall correlated with caseHigh correlation
VB has 21 (12.6%) missing values Missing
VB has 9 (5.4%) zeros Zeros
time has 5 (3.0%) zeros Zeros

Reproduction

Analysis started2025-06-11 10:24:24.160647
Analysis finished2025-06-11 10:24:45.882540
Duration21.72 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

case
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3293413
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:45.963129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median10
Q312
95-th percentile15
Maximum16
Range15
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.7648855
Coefficient of variation (CV)0.5720603
Kurtosis-1.345097
Mean8.3293413
Median Absolute Deviation (MAD)3
Skewness-0.22536555
Sum1391
Variance22.704134
MonotonicityNot monotonic
2025-06-11T10:24:46.053029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11 23
13.8%
1 17
10.2%
12 15
9.0%
13 15
9.0%
3 14
8.4%
2 14
8.4%
10 10
 
6.0%
9 9
 
5.4%
14 9
 
5.4%
7 8
 
4.8%
Other values (6) 33
19.8%
ValueCountFrequency (%)
1 17
10.2%
2 14
8.4%
3 14
8.4%
4 7
4.2%
5 6
 
3.6%
6 1
 
0.6%
7 8
4.8%
8 6
 
3.6%
9 9
5.4%
10 10
6.0%
ValueCountFrequency (%)
16 6
 
3.6%
15 7
 
4.2%
14 9
 
5.4%
13 15
9.0%
12 15
9.0%
11 23
13.8%
10 10
6.0%
9 9
 
5.4%
8 6
 
3.6%
7 8
 
4.8%

run
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1676647
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:46.148520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile16
Maximum23
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9693672
Coefficient of variation (CV)0.69330353
Kurtosis0.3072746
Mean7.1676647
Median Absolute Deviation (MAD)3
Skewness0.89041066
Sum1197
Variance24.694611
MonotonicityNot monotonic
2025-06-11T10:24:46.244369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 16
9.6%
2 15
 
9.0%
3 15
 
9.0%
5 15
 
9.0%
6 15
 
9.0%
4 14
 
8.4%
7 11
 
6.6%
8 10
 
6.0%
9 9
 
5.4%
10 8
 
4.8%
Other values (13) 39
23.4%
ValueCountFrequency (%)
1 16
9.6%
2 15
9.0%
3 15
9.0%
4 14
8.4%
5 15
9.0%
6 15
9.0%
7 11
6.6%
8 10
6.0%
9 9
5.4%
10 8
4.8%
ValueCountFrequency (%)
23 1
 
0.6%
22 1
 
0.6%
21 1
 
0.6%
20 1
 
0.6%
19 1
 
0.6%
18 1
 
0.6%
17 2
 
1.2%
16 3
1.8%
15 5
3.0%
14 6
3.6%

VB
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct64
Distinct (%)43.8%
Missing21
Missing (%)12.6%
Infinite0
Infinite (%)0.0%
Mean0.33760274
Minimum0
Maximum1.53
Zeros9
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:46.366766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.15
median0.285
Q30.4675
95-th percentile0.7975
Maximum1.53
Range1.53
Interquartile range (IQR)0.3175

Descriptive statistics

Standard deviation0.26052812
Coefficient of variation (CV)0.77170026
Kurtosis3.5991718
Mean0.33760274
Median Absolute Deviation (MAD)0.155
Skewness1.4997294
Sum49.29
Variance0.067874903
MonotonicityNot monotonic
2025-06-11T10:24:46.513538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
5.4%
0.2 5
 
3.0%
0.24 5
 
3.0%
0.08 5
 
3.0%
0.13 5
 
3.0%
0.38 4
 
2.4%
0.44 4
 
2.4%
0.14 4
 
2.4%
0.17 4
 
2.4%
0.4 4
 
2.4%
Other values (54) 97
58.1%
(Missing) 21
 
12.6%
ValueCountFrequency (%)
0 9
5.4%
0.04 2
 
1.2%
0.05 1
 
0.6%
0.07 2
 
1.2%
0.08 5
3.0%
0.09 3
 
1.8%
0.1 2
 
1.2%
0.11 1
 
0.6%
0.12 2
 
1.2%
0.13 5
3.0%
ValueCountFrequency (%)
1.53 1
0.6%
1.3 1
0.6%
1.14 1
0.6%
1.07 1
0.6%
0.92 1
0.6%
0.83 1
0.6%
0.81 2
1.2%
0.76 1
0.6%
0.74 1
0.6%
0.7 2
1.2%

time
Real number (ℝ)

High correlation  Zeros 

Distinct62
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.113772
Minimum0
Maximum105
Zeros5
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:46.650944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17.5
median19
Q339
95-th percentile72
Maximum105
Range105
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation23.292643
Coefficient of variation (CV)0.89196776
Kurtosis0.72427548
Mean26.113772
Median Absolute Deviation (MAD)15
Skewness1.0919287
Sum4361
Variance542.54722
MonotonicityNot monotonic
2025-06-11T10:24:46.886402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 12
 
7.2%
9 10
 
6.0%
6 8
 
4.8%
1 8
 
4.8%
19 6
 
3.6%
12 6
 
3.6%
45 6
 
3.6%
15 6
 
3.6%
21 6
 
3.6%
27 5
 
3.0%
Other values (52) 94
56.3%
ValueCountFrequency (%)
0 5
3.0%
1 8
4.8%
2 3
 
1.8%
3 12
7.2%
4 4
 
2.4%
6 8
4.8%
7 2
 
1.2%
8 1
 
0.6%
9 10
6.0%
10 2
 
1.2%
ValueCountFrequency (%)
105 1
0.6%
100 1
0.6%
93 1
0.6%
86 1
0.6%
81 1
0.6%
80 1
0.6%
75 1
0.6%
74 1
0.6%
72 2
1.2%
69 1
0.6%

DOC
Categorical

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
0.75
104 
1.5
63 

Length

Max length4
Median length4
Mean length3.6227545
Min length3

Characters and Unicode

Total characters605
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.5
2nd row1.5
3rd row1.5
4th row1.5
5th row1.5

Common Values

ValueCountFrequency (%)
0.75 104
62.3%
1.5 63
37.7%

Length

2025-06-11T10:24:47.070855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T10:24:47.169111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.75 104
62.3%
1.5 63
37.7%

Most occurring characters

ValueCountFrequency (%)
. 167
27.6%
5 167
27.6%
0 104
17.2%
7 104
17.2%
1 63
 
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 167
27.6%
5 167
27.6%
0 104
17.2%
7 104
17.2%
1 63
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 167
27.6%
5 167
27.6%
0 104
17.2%
7 104
17.2%
1 63
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 167
27.6%
5 167
27.6%
0 104
17.2%
7 104
17.2%
1 63
 
10.4%

feed
Categorical

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
0.25
85 
0.5
82 

Length

Max length4
Median length4
Mean length3.508982
Min length3

Characters and Unicode

Total characters586
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.25 85
50.9%
0.5 82
49.1%

Length

2025-06-11T10:24:47.276093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T10:24:47.360121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.25 85
50.9%
0.5 82
49.1%

Most occurring characters

ValueCountFrequency (%)
0 167
28.5%
. 167
28.5%
5 167
28.5%
2 85
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 167
28.5%
. 167
28.5%
5 167
28.5%
2 85
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 167
28.5%
. 167
28.5%
5 167
28.5%
2 85
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 167
28.5%
. 167
28.5%
5 167
28.5%
2 85
14.5%

material
Categorical

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
1
109 
2
58 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters167
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 109
65.3%
2 58
34.7%

Length

2025-06-11T10:24:47.470521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T10:24:47.570475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 109
65.3%
2 58
34.7%

Most occurring characters

ValueCountFrequency (%)
1 109
65.3%
2 58
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 167
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 109
65.3%
2 58
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 167
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 109
65.3%
2 58
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 167
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 109
65.3%
2 58
34.7%

smcAC
Real number (ℝ)

Distinct124
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16449523
Minimum-0.79833984
Maximum0.37841797
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)59.9%
Memory size1.4 KiB
2025-06-11T10:24:47.706482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.79833984
5-th percentile-0.67407227
Q1-0.55175781
median-0.15625
Q30.20629883
95-th percentile0.29711914
Maximum0.37841797
Range1.1767578
Interquartile range (IQR)0.75805664

Descriptive statistics

Standard deviation0.35772061
Coefficient of variation (CV)-2.1746565
Kurtosis-1.4963542
Mean-0.16449523
Median Absolute Deviation (MAD)0.36865234
Skewness-0.10947938
Sum-27.470703
Variance0.12796404
MonotonicityNot monotonic
2025-06-11T10:24:47.901703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2490234375 6
 
3.6%
0.21484375 5
 
3.0%
0.2783203125 3
 
1.8%
-0.595703125 3
 
1.8%
0.2075195312 3
 
1.8%
-0.244140625 3
 
1.8%
0.2294921875 2
 
1.2%
0.126953125 2
 
1.2%
-0.625 2
 
1.2%
0.205078125 2
 
1.2%
Other values (114) 136
81.4%
ValueCountFrequency (%)
-0.7983398438 1
0.6%
-0.751953125 1
0.6%
-0.7250976562 1
0.6%
-0.7177734375 1
0.6%
-0.7153320312 1
0.6%
-0.68359375 1
0.6%
-0.6811523438 1
0.6%
-0.6787109375 1
0.6%
-0.6762695312 1
0.6%
-0.6689453125 1
0.6%
ValueCountFrequency (%)
0.3784179688 1
0.6%
0.341796875 2
1.2%
0.3369140625 1
0.6%
0.3295898438 1
0.6%
0.3271484375 1
0.6%
0.3076171875 1
0.6%
0.3002929688 1
0.6%
0.2978515625 1
0.6%
0.2954101562 2
1.2%
0.2856445312 1
0.6%

smcDC
Real number (ℝ)

Distinct37
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3365457
Minimum2.0846772 × 10-33
Maximum1.4501953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:48.075648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.0846772 × 10-33
5-th percentile1.2988281
Q11.3500977
median1.3720703
Q31.3916016
95-th percentile1.4208984
Maximum1.4501953
Range1.4501953
Interquartile range (IQR)0.041503906

Descriptive statistics

Standard deviation0.18938803
Coefficient of variation (CV)0.14169963
Kurtosis28.929708
Mean1.3365457
Median Absolute Deviation (MAD)0.01953125
Skewness-5.2257343
Sum223.20312
Variance0.035867825
MonotonicityNot monotonic
2025-06-11T10:24:48.243793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1.381835938 14
 
8.4%
1.352539062 12
 
7.2%
1.357421875 12
 
7.2%
1.38671875 10
 
6.0%
1.3671875 10
 
6.0%
1.391601562 9
 
5.4%
1.401367188 8
 
4.8%
1.376953125 8
 
4.8%
1.333007812 8
 
4.8%
1.342773438 7
 
4.2%
Other values (27) 69
41.3%
ValueCountFrequency (%)
2.08467715 × 10-331
0.6%
0.1318359375 1
0.6%
0.44921875 1
0.6%
0.625 1
0.6%
0.6689453125 1
0.6%
0.68359375 1
0.6%
0.9130859375 1
0.6%
1.2890625 1
0.6%
1.298828125 2
1.2%
1.303710938 1
0.6%
ValueCountFrequency (%)
1.450195312 2
 
1.2%
1.440429688 1
 
0.6%
1.435546875 1
 
0.6%
1.430664062 1
 
0.6%
1.42578125 3
 
1.8%
1.420898438 2
 
1.2%
1.416015625 4
2.4%
1.411132812 6
3.6%
1.40625 7
4.2%
1.401367188 8
4.8%

vib_table
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.079484586
Minimum1.0546774 × 10-8
Maximum0.29296875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:48.414509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0546774 × 10-8
5-th percentile0.053710938
Q10.061035156
median0.068359375
Q30.083007812
95-th percentile0.17504883
Maximum0.29296875
Range0.29296874
Interquartile range (IQR)0.021972656

Descriptive statistics

Standard deviation0.036542182
Coefficient of variation (CV)0.45973923
Kurtosis8.8525977
Mean0.079484586
Median Absolute Deviation (MAD)0.009765625
Skewness2.6350568
Sum13.273926
Variance0.0013353311
MonotonicityNot monotonic
2025-06-11T10:24:48.605664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.06103515625 16
 
9.6%
0.05859375 14
 
8.4%
0.0634765625 14
 
8.4%
0.068359375 13
 
7.8%
0.07080078125 12
 
7.2%
0.0537109375 10
 
6.0%
0.05615234375 10
 
6.0%
0.06591796875 9
 
5.4%
0.078125 8
 
4.8%
0.08544921875 5
 
3.0%
Other values (30) 56
33.5%
ValueCountFrequency (%)
1.054677412 × 10-81
 
0.6%
0.04150390625 1
 
0.6%
0.05126953125 4
 
2.4%
0.0537109375 10
6.0%
0.05615234375 10
6.0%
0.05859375 14
8.4%
0.06103515625 16
9.6%
0.0634765625 14
8.4%
0.06591796875 9
5.4%
0.068359375 13
7.8%
ValueCountFrequency (%)
0.29296875 1
0.6%
0.1953125 1
0.6%
0.1904296875 1
0.6%
0.1879882812 2
1.2%
0.1831054688 1
0.6%
0.1782226562 1
0.6%
0.17578125 2
1.2%
0.1733398438 1
0.6%
0.1708984375 1
0.6%
0.1684570312 1
0.6%

vib_spindle
Real number (ℝ)

Distinct63
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28565186
Minimum2.6976452 × 10-6
Maximum0.3918457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:48.804630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.6976452 × 10-6
5-th percentile0.26000977
Q10.2746582
median0.28442383
Q30.29785156
95-th percentile0.3203125
Maximum0.3918457
Range0.39184301
Interquartile range (IQR)0.023193359

Descriptive statistics

Standard deviation0.030757826
Coefficient of variation (CV)0.10767592
Kurtosis45.270688
Mean0.28565186
Median Absolute Deviation (MAD)0.010986328
Skewness-4.4785519
Sum47.70386
Variance0.00094604384
MonotonicityNot monotonic
2025-06-11T10:24:49.040245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2844238281 8
 
4.8%
0.3015136719 7
 
4.2%
0.2893066406 7
 
4.2%
0.2856445312 6
 
3.6%
0.2746582031 6
 
3.6%
0.2758789062 6
 
3.6%
0.2795410156 6
 
3.6%
0.29296875 5
 
3.0%
0.2648925781 5
 
3.0%
0.2941894531 5
 
3.0%
Other values (53) 106
63.5%
ValueCountFrequency (%)
2.697645186 × 10-61
 
0.6%
0.2075195312 1
 
0.6%
0.2502441406 1
 
0.6%
0.2526855469 1
 
0.6%
0.25390625 1
 
0.6%
0.2551269531 1
 
0.6%
0.2563476562 1
 
0.6%
0.2575683594 1
 
0.6%
0.2600097656 3
1.8%
0.2612304688 2
1.2%
ValueCountFrequency (%)
0.3918457031 1
 
0.6%
0.3649902344 1
 
0.6%
0.3515625 1
 
0.6%
0.3454589844 1
 
0.6%
0.3308105469 1
 
0.6%
0.322265625 1
 
0.6%
0.3210449219 3
1.8%
0.3186035156 1
 
0.6%
0.3173828125 1
 
0.6%
0.3161621094 1
 
0.6%

AE_table
Real number (ℝ)

High correlation 

Distinct82
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10042293
Minimum4.0746264 × 10-11
Maximum0.14953613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:49.264928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.0746264 × 10-11
5-th percentile0.037475586
Q10.092773438
median0.10192871
Q30.11474609
95-th percentile0.13061523
Maximum0.14953613
Range0.14953613
Interquartile range (IQR)0.021972656

Descriptive statistics

Standard deviation0.024699533
Coefficient of variation (CV)0.2459551
Kurtosis5.0419993
Mean0.10042293
Median Absolute Deviation (MAD)0.010375977
Skewness-1.8635497
Sum16.77063
Variance0.00061006693
MonotonicityNot monotonic
2025-06-11T10:24:49.411251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0927734375 6
 
3.6%
0.09582519531 5
 
3.0%
0.09887695312 5
 
3.0%
0.1025390625 5
 
3.0%
0.1098632812 5
 
3.0%
0.09643554688 4
 
2.4%
0.01770019531 4
 
2.4%
0.09399414062 4
 
2.4%
0.09765625 4
 
2.4%
0.1110839844 4
 
2.4%
Other values (72) 121
72.5%
ValueCountFrequency (%)
4.0746264 × 10-111
 
0.6%
0.01770019531 4
2.4%
0.01831054688 1
 
0.6%
0.02075195312 1
 
0.6%
0.02136230469 2
1.2%
0.07507324219 1
 
0.6%
0.07568359375 1
 
0.6%
0.07690429688 1
 
0.6%
0.078125 2
1.2%
0.08239746094 3
1.8%
ValueCountFrequency (%)
0.1495361328 1
 
0.6%
0.1452636719 1
 
0.6%
0.1354980469 1
 
0.6%
0.1336669922 2
1.2%
0.1330566406 2
1.2%
0.1318359375 1
 
0.6%
0.1306152344 2
1.2%
0.1300048828 2
1.2%
0.1281738281 1
 
0.6%
0.126953125 3
1.8%

AE_spindle
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12387214
Minimum2.7002554 × 10-6
Maximum0.18127441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-06-11T10:24:49.545474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.7002554 × 10-6
5-th percentile0.098022461
Q10.11047363
median0.12207031
Q30.13641357
95-th percentile0.16052246
Maximum0.18127441
Range0.18127171
Interquartile range (IQR)0.025939941

Descriptive statistics

Standard deviation0.021194075
Coefficient of variation (CV)0.17109638
Kurtosis6.0996157
Mean0.12387214
Median Absolute Deviation (MAD)0.013427734
Skewness-0.75505393
Sum20.686648
Variance0.00044918882
MonotonicityNot monotonic
2025-06-11T10:24:49.677465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1104736328 7
 
4.2%
0.1165771484 6
 
3.6%
0.1232910156 5
 
3.0%
0.1397705078 4
 
2.4%
0.1208496094 3
 
1.8%
0.1239013672 3
 
1.8%
0.1049804688 3
 
1.8%
0.1068115234 3
 
1.8%
0.1342773438 3
 
1.8%
0.1062011719 3
 
1.8%
Other values (81) 127
76.0%
ValueCountFrequency (%)
2.700255436 × 10-61
0.6%
0.0830078125 1
0.6%
0.09399414062 2
1.2%
0.09460449219 2
1.2%
0.09521484375 1
0.6%
0.09582519531 1
0.6%
0.09765625 1
0.6%
0.09887695312 2
1.2%
0.09948730469 1
0.6%
0.1000976562 1
0.6%
ValueCountFrequency (%)
0.1812744141 1
 
0.6%
0.1715087891 1
 
0.6%
0.1678466797 1
 
0.6%
0.1641845703 2
1.2%
0.1629638672 1
 
0.6%
0.1611328125 1
 
0.6%
0.1605224609 3
1.8%
0.1599121094 1
 
0.6%
0.1586914062 1
 
0.6%
0.1568603516 1
 
0.6%

Interactions

2025-06-11T10:24:44.413414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:24.813537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:27.121400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:29.275915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:31.678436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:35.214032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:38.396356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:40.395859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.464567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.456523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.505615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:24.991170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:27.275710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:29.527190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:31.825306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:35.610845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:38.555921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:40.669371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.559880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.546681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.590339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:25.124478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:27.525718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:29.708290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:32.032148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:36.055833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:38.729746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:40.907190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.648201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.632957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.686745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:25.273724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:27.750473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:30.039930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:32.584063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:36.809650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:38.873895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:41.157336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.750694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.726683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.776468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:25.452785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:27.951977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:30.366428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:32.841144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:37.108820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:39.043671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:41.768024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.846681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.818811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.870224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:25.663542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:28.140029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:30.530708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:33.302303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:37.324719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:39.291907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:41.981951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.943659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.910809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.960238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:25.861045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:28.373210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:30.790953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:33.717111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:37.606156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:39.604456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.072166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.044252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.010834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:45.054823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:26.119084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:28.542771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:31.050043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:34.074214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:37.778703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:39.812303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.170215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.139302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.107987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:45.153187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:26.614839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:28.715532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:31.281297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:34.402206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:37.941267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:39.994890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.261925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.241269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.211949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:45.247950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:26.934898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:28.944796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:31.493181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:34.797108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:38.149921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:40.184107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:42.369160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:43.344357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T10:24:44.299927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-11T10:24:49.808049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AE_spindleAE_tableDOCVBcasefeedmaterialrunsmcACsmcDCtimevib_spindlevib_table
AE_spindle1.0000.9240.000-0.134-0.1220.1820.2440.033-0.082-0.0180.145-0.0970.216
AE_table0.9241.0000.096-0.091-0.0990.1810.2650.048-0.072-0.0110.159-0.0670.196
DOC0.0000.0961.0000.0000.7820.1430.0000.1450.0000.1520.1860.2630.137
VB-0.134-0.0910.0001.0000.1950.0000.1940.7450.0040.2130.669-0.358-0.091
case-0.122-0.0990.7820.1951.0000.7610.867-0.072-0.065-0.133-0.159-0.259-0.512
feed0.1820.1810.1430.0000.7611.0000.0000.0000.0000.1240.0000.1460.188
material0.2440.2650.0000.1940.8670.0001.0000.2020.1040.0970.4110.0430.291
run0.0330.0480.1450.745-0.0720.0000.2021.000-0.0070.3590.952-0.2540.169
smcAC-0.082-0.0720.0000.004-0.0650.0000.104-0.0071.0000.2830.011-0.0560.014
smcDC-0.018-0.0110.1520.213-0.1330.1240.0970.3590.2831.0000.367-0.0910.072
time0.1450.1590.1860.669-0.1590.0000.4110.9520.0110.3671.000-0.2650.223
vib_spindle-0.097-0.0670.263-0.358-0.2590.1460.043-0.254-0.056-0.091-0.2651.0000.038
vib_table0.2160.1960.137-0.091-0.5120.1880.2910.1690.0140.0720.2230.0381.000

Missing values

2025-06-11T10:24:45.682267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-11T10:24:45.813771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

caserunVBtimeDOCfeedmaterialsmcACsmcDCvib_tablevib_spindleAE_tableAE_spindle
0110.0021.50.51-0.0170900.6250000.0781250.3149410.0872800.103760
112NaN41.50.510.3076170.6689450.0756840.3015140.0866700.099487
213NaN61.50.51-0.7250980.9130860.0830080.2954100.0927730.104980
3140.1171.50.510.1123050.1318360.0830080.3161620.1129150.139771
415NaN111.50.51-0.1220700.4492190.1074220.2844240.0958250.110474
5160.20151.50.510.3295900.6835940.0708010.3076170.1037600.120239
6170.24191.50.51-0.5786131.3818360.0659180.3222660.0909420.123901
7180.29221.50.510.3002931.4355470.0610350.3088380.0927730.108643
8190.28261.50.51-0.3051761.4501950.0659180.2844240.0842290.098877
91100.29291.50.510.2148441.4062500.0610350.2795410.0878910.106812
caserunVBtimeDOCfeedmaterialsmcACsmcDCvib_tablevib_spindleAE_tableAE_spindle
1571540.3791.50.2520.1562501.3818360.0610350.2771000.0872800.101318
1581550.48131.50.252-0.6347661.3427730.0610350.2661130.1055910.123901
1591560.56161.50.252-0.1953121.4013670.0634770.2856450.0836180.093994
1601570.70191.50.252-0.6835941.3769530.0561520.2612300.1025390.123291
161161NaN11.50.502-0.1513671.3525390.0585940.3649900.0915530.098877
162162NaN21.50.502-0.5834961.3085940.0634770.3308110.0939940.109253
1631630.2431.50.502-0.2001951.4062500.0659180.2795410.1147460.139771
164164NaN41.50.5020.2441411.3281250.0634770.2905270.1013180.117798
1651650.4061.50.502-0.2050781.3818360.0683590.2893070.0988770.114746
1661660.6291.50.502-0.3808591.3818360.0415040.2929690.0756840.083008